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Record W4392400332 · doi:10.1504/ijesdf.2024.137045

A novel colour image encryption algorithm using S-box technique

2024· article· en· W4392400332 on OpenAlex
Kiran Shrimant Kakade, Swagata Sarkar, S. Asha, C. Sivakumaran

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInternational Journal of Electronic Security and Digital Forensics · 2024
Typearticle
Languageen
FieldComputer Science
TopicChaos-based Image/Signal Encryption
Canadian institutionsArtificial Intelligence in Medicine (Canada)
Fundersnot available
KeywordsComputer scienceEncryptionImage (mathematics)Artificial intelligenceAlgorithmComputer visionComputer graphics (images)Computer security

Abstract

fetched live from OpenAlex

The combined 3D image suggests that SHA-256 is responsible for seeding the memristive chaotic system with initial values. The suggested picture encryption method uses the encrypted image's output value to set the algorithm's parameters. Second, either discrete Arnold map or indeed the quantum chaotic maps are used to construct the structure of permutations and grey-level encryption, respectively. A classical chaos sequence modifies the pixel value before it is permuted using the Arnold transform. We use the S-box to introduce nonlinearity and diffusion to image files, and then we use the Boolean function XOR to the encrypted picture to provide even more randomness. Additionally, we examine randomness tests such as NIST-R, correlation, and key evaluation. The efficiency of the proposed method is evaluated in relation to many similar existing algorithms. Both theoretical and practical studies support the reliability of our methodology.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.908
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.003
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.008
GPT teacher head0.267
Teacher spread0.259 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it